Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease

• Published in: IEEE access Vol. 7; pp. 43721 - 43729
• Main Authors: Singh, Uday Pratap, Chouhan, Siddharth Singh, Jain, Sukirty, Jain, Sanjeev
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Agriculture; Artificial neural networks; Biological system modeling; Classification; Computer vision; Convolution; Convolutional neural network; Convolutional neural networks; Datasets; Deep learning; Disease; Diseases; Fungal diseases; Fungi; Image classification; Leaves; Machine learning; Mangoes; Medical imaging; Model accuracy; Multilayers; Neural networks; plant pathology; precision agriculture; Real-time systems; Signs and symptoms; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2907383

Fungal diseases not only influence the economic importance of the plants and its products but also abate their ecological prominence. Mango tree, specifically the fruits and the leaves are highly affected by the fungal disease named as Anthracnose . The main aim of this paper is to develop an appropriate and effective method for diagnosis of the disease and its symptoms, therefore espousing a suitable system for an early and cost-effective solution of this problem. Over the last few years, due to their higher performance capability in terms of computation and accuracy, computer vision, and deep learning methodologies have gained popularity in assorted fungal diseases classification. Therefore, for this paper, a multilayer convolutional neural network (MCNN) is proposed for the classification of the Mango leaves infected by the Anthracnose fungal disease. This paper is validated on a real-time dataset captured at the Shri Mata Vaishno Devi University, Katra, J&K, India consists of 1070 images of the Mango tree leaves. The dataset contains both healthy and infected leaf images. The results envisage the higher classification accuracy of the proposed MCNN model when compared to the other state-of-the-art approaches.